This book isnot a complete introduction to statistical theory. It should also not be thefirst statistics book you read. Instead, this book shows you how to implementmany useful data analysis routines in R. Sometimes we explain a bit of theory behind the method, but this is anexception. We really assume that you have already learned statistics elsewhereand are here to learn how to actually do the analyses.

We havelearned from experience that data practictioners, that means scientists andeveryone else who has an interest in learning from data, do not learnstatistical analyses by studying the underlying theory. They learn from doing,and from using examples written by others who have learned by doing. For thisreason the book in front of you is largely a compendium of examples. We havechosen the examples and the exercises in the hope that they resemble real-worldproblems.

We havedesigned this book so it can be used for self-study. The exercises at the endof each chapter aim to test important skills learned in the chapter. Before youstart on the exercises, read through the text, try to run the examples, andplay around with the code making small modifications. We have also placed many Try it yourself boxes throughout thetext to give you some ideas on what to try, but try to be creative and modifythe examples to see what happens.

Acknowledgments
Thisbook accompanies the five-day course ’Data analysis and visualization with R’,taught twice a year at the Hawkesbury Institute for the Environment (www.westernsydney.edu.au/hie).The PDF of this book is available at www.westernsydney.edu.au/rmanual. Theclass website can be reached at bit.ly/RDataCourse.

We have used knitr(with Sweave and LATEX) (see http://yihui.name/knitr/)to produce this book, from within Rstudio (www.rstudio.com). We also use numerous add-onpackages. Thanks to the developers for making these tools freely available.
September 5, 2018